AIMC Topic: Magnetic Resonance Imaging
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Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.
The international journal of cardiovascular imaging
Nov 7, 2024
Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to...
Machine-learning based prediction of future outcome using multimodal MRI during early childhood.
Seminars in fetal & neonatal medicine
Nov 7, 2024
The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging ...
The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Nov 6, 2024
BACKGROUND: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in ...
Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified Fast 3D mode wheel.
Japanese journal of radiology
Nov 6, 2024
PURPOSE: To assess the image quality of a modified Fast three-dimensional (Fast 3D) mode wheel with sequential data filling (mFast 3D wheel) combined with a deep learning denoising technique (Advanced Intelligent Clear-IQ Engine [AiCE]) in contrast-e...
Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Nov 6, 2024
Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing model...
A simple but tough-to-beat baseline for fMRI time-series classification.
NeuroImage
Nov 6, 2024
Current neuroimaging studies frequently use complex machine learning models to classify human fMRI data, distinguishing healthy and disordered brains, often to validate new methods or enhance prediction accuracy. Yet, where prediction accuracy is a c...
Automated detection of bone lesions using CT and MRI: a systematic review.
La Radiologia medica
Nov 6, 2024
PURPOSE: The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications.
Deep learning in image segmentation for cancer.
Journal of medical radiation sciences
Nov 6, 2024
This article discusses the role of deep learning (DL) in cancer imaging, focusing on its applications for automatic image segmentation. It highlights two studies that demonstrate how U-Net- and convolutional neural networks-based architectures have i...
Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.
Scientific reports
Nov 6, 2024
It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residua...
Classification of Multi-Parametric Body MRI Series Using Deep Learning.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional t...
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